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Papers by Dhanushka Kularatne

Research paper thumbnail of Small and Adrift with Self-Control: Using the Environment to Improve Autonomy

Springer Proceedings in Advanced Robotics, 2017

Research paper thumbnail of A Topological Approach to Path Planning for a Magnetic Millirobot

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020

Research paper thumbnail of Optimal Paths in Time-Varying Flow Fields

The use of autonomous marine vehicles (AMV) have seen a significant growth in the last few decade... more The use of autonomous marine vehicles (AMV) have seen a significant growth in the last few decades. This growth has been driven not only by advances in vehicle technology, but also by a need from the scientific and industrial communities for increased autonomy in marine environments. The use of AMVs for scientific activities in aquatic environments has increased data availability, reliability and consistency while their use in commercial activities has made marine based systems safer and more reliable. AMVs have been used for scientific activities such as migration tracking, measurement of temperature and salinity profiles, and monitoring of harmful algae blooms. AMVs have also been used in mapping and characterizing ocean structures, monitoring submerged pipelines and power transmission cables, locating plane crash and ship wreckage, and assessing oil spills. They have helped us to better understand oceanic processes ranging from biological phenomena to climate change. However, these platforms are generally small and resource constrained. While this helps with maneuverability and allows for unobtrusive monitoring of marine phenomena, it also means that the AMV missions have limited durations. To increase the utility of these vehicles and further increase their adoption in marine applications, their mission durations have to be made longer. To this end, developers of AMVs are actively looking at ways of optimizing on-board resource usage, from using better senors and computers to more efficient hull designs. In this work, efficient navigation is explored as an alternate method to improve mission durations. The high inertia environments that AMVs operate in presents a unique opportunity for vehicles to exploit the surrounding flows for more efficient navigation. Trajectories of AMVs should try to use a 'go with the flow' strategy to reduce its energy consumption. To this end, the thesis considers the general problem of optimal path planning in timevarying flow fields for resource constrained marine vehicles. A gra [...]

Research paper thumbnail of Design and validation of a micro-AUV for 3-D sampling of coherent ocean features

OCEANS 2015 - MTS/IEEE Washington, 2015

The ocean, as vast as it is complex, has a plethora of phenomena that are of legitimate scientifi... more The ocean, as vast as it is complex, has a plethora of phenomena that are of legitimate scientific interest, e.g., ocean fronts and Lagrangian Coherent Structures. These coherent ocean features occur from tidal mixing and ocean circulation, and are generally characterized with narrow bands of locally intensive physical gradients with enhanced circulation, biological productivity, and optimal transport phenomena. Spatial extents of these phenomena can be on the order of 10's of km2, and episodic events can last from hours to weeks. These ocean features are 3-dimensional, where to date, most research has focused on examining only their 2-dimensional expression. These coherent features cannot be thoroughly studied through traditional sampling involving random and/or discrete sampling approaches, moreover it is not cost-effective to validate new sampling methodologies in the field. Additionally, operating a single robotic platform in the ocean is hard, and coordinating a team of rob...

Research paper thumbnail of Adaptive sampling and energy-efficient navigation in time-varying flows

Autonomous Underwater Vehicles: Design and practice, 2020

This chapter presents a strategy to enable a team of mobile robots to adaptively sample and track... more This chapter presents a strategy to enable a team of mobile robots to adaptively sample and track a dynamic spatiotemporal process. We propose a distributed strategy where robots collect sparse sensor measurements, create a reduced -order model (ROM) of the spatiotemporal process, and use this model to estimate field values for areas without sensor measurements of the dynamic process. The robots then use these estimates of the field, or inferences about the process, to adapt the model and reconfigure their sensing locations. We use this method to obtain an estimate for the underlying fl ow field and use that to plan optimal energy paths for robots to travel between sensing locations. We show that the errors due to the reduced -order modeling scheme are bounded, and we illustrate the application of the proposed solution in simulation and compare it to centralized and global approaches. We then test our approach with physical marine robots sampling a spatially nonuniform time -varying process in a water tank.

Research paper thumbnail of Computing Energy Optimal Paths in Time-Varying Flows

Autonomous marine vehicles (AMVs) are typically deployed for long periods of time in the ocean to... more Autonomous marine vehicles (AMVs) are typically deployed for long periods of time in the ocean to monitor different physical, chemical, and biological processes. Given their limited energy budgets, it makes sense to consider motion plans that leverage the dynamics of the surrounding flow field so as to minimize energy usage for these vehicles. In this paper, we present two graph search based methods to compute energy optimal paths for AMVs in two-dimensional (2-D) time-varying flows. The novelty of the proposed algorithms lies in a unique discrete graph representation of the 3-D configuration space spanned by the spatio-temporal coordinates. This enables a more efficient traversal through the search space, as opposed to a full search of the spatio-temporal configuration space. Furthermore, the proposed strategy results in solutions that are closer to the global optimal when compared to greedy searches through the spatial coordinates alone. We demonstrate the proposed algorithms by c...

Research paper thumbnail of Cooperative transport of a buoyant load: A differential geometric approach

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017

We present a differential geometric approach towards the synthesis of cooperative controllers for... more We present a differential geometric approach towards the synthesis of cooperative controllers for a team of autonomous surface vehicles transporting a buoyant load. We are interested in cooperative transport of large objects by teams of autonomous surface vehicles (ASVs) operating in marine and littoral environments. We consider the cooperative towing problem where individual ASVs connected to a load via cables must coordinate to transport the load along a desired trajectory. We present a differential geometric approach towards the synthesis of open and closed loop strategies for the team. The main advantage of the proposed strategy is the ability to synthesize agent-level controllers that can simultaneously satisfy all the holonomic and non-holonomic constraints within the system. We validate the approach in both simulations and experiments.

Research paper thumbnail of Optimal Path Planning in Time-Varying Flows with Forecasting Uncertainties

2018 IEEE International Conference on Robotics and Automation (ICRA), 2018

Uncertainties in flow models have to be explicitly considered for effective path planning in mari... more Uncertainties in flow models have to be explicitly considered for effective path planning in marine environments. In this paper, we present two methods to compute minimum expected cost policies and paths over an uncertain flow model. The first method based on a Markov Decision Process computes a minimum expected cost policy while the second graph search based method, computes a minimum expected cost path. A transition probability model is developed to compute the probability of transition from one state to another under a given action. In addition, a method to compute the expected cost of a path when it is executed in an uncertain flow field is also presented. The two methods are used to compute minimum energy paths in an ocean environment and the results are analyzed in simulations.

Research paper thumbnail of Bridging the gap: Machine learning to resolve improperly modeled dynamics

Physica D: Nonlinear Phenomena, 2020

Research paper thumbnail of Differential Geometric Approach to Trajectory Planning: Cooperative Transport by a Team of Autonomous Marine Vehicles

2018 Annual American Control Conference (ACC), Jun 1, 2018

In this paper we addressed the cooperative transport problem for a team of autonomous surface veh... more In this paper we addressed the cooperative transport problem for a team of autonomous surface vehicles (ASVs) towing a single buoyant load. We consider the dynamics of the constrained system and decompose the cooperative transport problem into a collection of subproblems. Each subproblem consists of an ASV and load pair where each ASV is attached to the load at the same point. Since the system states evolve on a smooth manifold, we use the tools from differential geometry to model the holonomic constraint arising from the cooperative transport problem and the non-holonomic constraints arising from the ASV dynamics. We then synthesize distributed feedback control strategies using the proposed mathematical modeling framework to enable the team transport the load on a desired trajectory. We experimentally validate the proposed strategy using a team of micro ASVs.

Research paper thumbnail of Using control to shape stochastic escape and switching dynamics

Chaos: An Interdisciplinary Journal of Nonlinear Science, 2019

We present a strategy to control the mean stochastic switching times of general dynamical systems... more We present a strategy to control the mean stochastic switching times of general dynamical systems with multiple equilibrium states subject to Gaussian white noise. The control can either enhance or abate the probability of escape from the deterministic region of attraction of a stable equilibrium in the presence of external noise. We synthesize a feedback control strategy that actively changes the system's mean stochastic switching behavior based on the system's distance to the boundary of the attracting region. With the proposed controller, we are able to achieve a desired mean switching time, even when the strength of noise in the system is not known. The control method is analytically validated using a one-dimensional system, and its e ectiveness is numerically demonstrated for a set of dynamical systems of practical importance.

Research paper thumbnail of Optimal Path Planning in Time-Varying Flows Using Adaptive Discretization

IEEE Robotics and Automation Letters, 2018

Research paper thumbnail of Going with the flow: a graph based approach to optimal path planning in general flows

Research paper thumbnail of Tracking attracting manifolds in flows

Research paper thumbnail of Time and Energy Optimal Path Planning in General Flows

Robotics: Science and Systems XII

Research paper thumbnail of Tracking Attracting Lagrangian Coherent Structures in Flows

Robotics: Science and Systems XI, 2015

Research paper thumbnail of Zig-zag wanderer: Towards adaptive tracking of time-varying coherent structures in the ocean

2015 IEEE International Conference on Robotics and Automation (ICRA), 2015

Research paper thumbnail of Exploiting Stochasticity for the Control of Transitions in Gyre Flows

Robotics: Science and Systems XIV, 2018

We present a control strategy to control the intergyre switching time of an agent operating in a ... more We present a control strategy to control the intergyre switching time of an agent operating in a gyre flow. The proposed control strategy exploits the stochasticity of the underlying environment to affect inter-gyre transitions. We show how control can be used to enhance or abate the mean escape time and present a strategy to achieve a desired mean escape time. We show that the proposed control strategy can achieve any desired escape time in an interval governed by the maximum available control. We demonstrate the effectiveness of the strategy in simulations.

Research paper thumbnail of Small and Adrift with Self-Control: Using the Environment to Improve Autonomy

Springer Proceedings in Advanced Robotics, 2017

Research paper thumbnail of A Topological Approach to Path Planning for a Magnetic Millirobot

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020

Research paper thumbnail of Optimal Paths in Time-Varying Flow Fields

The use of autonomous marine vehicles (AMV) have seen a significant growth in the last few decade... more The use of autonomous marine vehicles (AMV) have seen a significant growth in the last few decades. This growth has been driven not only by advances in vehicle technology, but also by a need from the scientific and industrial communities for increased autonomy in marine environments. The use of AMVs for scientific activities in aquatic environments has increased data availability, reliability and consistency while their use in commercial activities has made marine based systems safer and more reliable. AMVs have been used for scientific activities such as migration tracking, measurement of temperature and salinity profiles, and monitoring of harmful algae blooms. AMVs have also been used in mapping and characterizing ocean structures, monitoring submerged pipelines and power transmission cables, locating plane crash and ship wreckage, and assessing oil spills. They have helped us to better understand oceanic processes ranging from biological phenomena to climate change. However, these platforms are generally small and resource constrained. While this helps with maneuverability and allows for unobtrusive monitoring of marine phenomena, it also means that the AMV missions have limited durations. To increase the utility of these vehicles and further increase their adoption in marine applications, their mission durations have to be made longer. To this end, developers of AMVs are actively looking at ways of optimizing on-board resource usage, from using better senors and computers to more efficient hull designs. In this work, efficient navigation is explored as an alternate method to improve mission durations. The high inertia environments that AMVs operate in presents a unique opportunity for vehicles to exploit the surrounding flows for more efficient navigation. Trajectories of AMVs should try to use a 'go with the flow' strategy to reduce its energy consumption. To this end, the thesis considers the general problem of optimal path planning in timevarying flow fields for resource constrained marine vehicles. A gra [...]

Research paper thumbnail of Design and validation of a micro-AUV for 3-D sampling of coherent ocean features

OCEANS 2015 - MTS/IEEE Washington, 2015

The ocean, as vast as it is complex, has a plethora of phenomena that are of legitimate scientifi... more The ocean, as vast as it is complex, has a plethora of phenomena that are of legitimate scientific interest, e.g., ocean fronts and Lagrangian Coherent Structures. These coherent ocean features occur from tidal mixing and ocean circulation, and are generally characterized with narrow bands of locally intensive physical gradients with enhanced circulation, biological productivity, and optimal transport phenomena. Spatial extents of these phenomena can be on the order of 10's of km2, and episodic events can last from hours to weeks. These ocean features are 3-dimensional, where to date, most research has focused on examining only their 2-dimensional expression. These coherent features cannot be thoroughly studied through traditional sampling involving random and/or discrete sampling approaches, moreover it is not cost-effective to validate new sampling methodologies in the field. Additionally, operating a single robotic platform in the ocean is hard, and coordinating a team of rob...

Research paper thumbnail of Adaptive sampling and energy-efficient navigation in time-varying flows

Autonomous Underwater Vehicles: Design and practice, 2020

This chapter presents a strategy to enable a team of mobile robots to adaptively sample and track... more This chapter presents a strategy to enable a team of mobile robots to adaptively sample and track a dynamic spatiotemporal process. We propose a distributed strategy where robots collect sparse sensor measurements, create a reduced -order model (ROM) of the spatiotemporal process, and use this model to estimate field values for areas without sensor measurements of the dynamic process. The robots then use these estimates of the field, or inferences about the process, to adapt the model and reconfigure their sensing locations. We use this method to obtain an estimate for the underlying fl ow field and use that to plan optimal energy paths for robots to travel between sensing locations. We show that the errors due to the reduced -order modeling scheme are bounded, and we illustrate the application of the proposed solution in simulation and compare it to centralized and global approaches. We then test our approach with physical marine robots sampling a spatially nonuniform time -varying process in a water tank.

Research paper thumbnail of Computing Energy Optimal Paths in Time-Varying Flows

Autonomous marine vehicles (AMVs) are typically deployed for long periods of time in the ocean to... more Autonomous marine vehicles (AMVs) are typically deployed for long periods of time in the ocean to monitor different physical, chemical, and biological processes. Given their limited energy budgets, it makes sense to consider motion plans that leverage the dynamics of the surrounding flow field so as to minimize energy usage for these vehicles. In this paper, we present two graph search based methods to compute energy optimal paths for AMVs in two-dimensional (2-D) time-varying flows. The novelty of the proposed algorithms lies in a unique discrete graph representation of the 3-D configuration space spanned by the spatio-temporal coordinates. This enables a more efficient traversal through the search space, as opposed to a full search of the spatio-temporal configuration space. Furthermore, the proposed strategy results in solutions that are closer to the global optimal when compared to greedy searches through the spatial coordinates alone. We demonstrate the proposed algorithms by c...

Research paper thumbnail of Cooperative transport of a buoyant load: A differential geometric approach

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017

We present a differential geometric approach towards the synthesis of cooperative controllers for... more We present a differential geometric approach towards the synthesis of cooperative controllers for a team of autonomous surface vehicles transporting a buoyant load. We are interested in cooperative transport of large objects by teams of autonomous surface vehicles (ASVs) operating in marine and littoral environments. We consider the cooperative towing problem where individual ASVs connected to a load via cables must coordinate to transport the load along a desired trajectory. We present a differential geometric approach towards the synthesis of open and closed loop strategies for the team. The main advantage of the proposed strategy is the ability to synthesize agent-level controllers that can simultaneously satisfy all the holonomic and non-holonomic constraints within the system. We validate the approach in both simulations and experiments.

Research paper thumbnail of Optimal Path Planning in Time-Varying Flows with Forecasting Uncertainties

2018 IEEE International Conference on Robotics and Automation (ICRA), 2018

Uncertainties in flow models have to be explicitly considered for effective path planning in mari... more Uncertainties in flow models have to be explicitly considered for effective path planning in marine environments. In this paper, we present two methods to compute minimum expected cost policies and paths over an uncertain flow model. The first method based on a Markov Decision Process computes a minimum expected cost policy while the second graph search based method, computes a minimum expected cost path. A transition probability model is developed to compute the probability of transition from one state to another under a given action. In addition, a method to compute the expected cost of a path when it is executed in an uncertain flow field is also presented. The two methods are used to compute minimum energy paths in an ocean environment and the results are analyzed in simulations.

Research paper thumbnail of Bridging the gap: Machine learning to resolve improperly modeled dynamics

Physica D: Nonlinear Phenomena, 2020

Research paper thumbnail of Differential Geometric Approach to Trajectory Planning: Cooperative Transport by a Team of Autonomous Marine Vehicles

2018 Annual American Control Conference (ACC), Jun 1, 2018

In this paper we addressed the cooperative transport problem for a team of autonomous surface veh... more In this paper we addressed the cooperative transport problem for a team of autonomous surface vehicles (ASVs) towing a single buoyant load. We consider the dynamics of the constrained system and decompose the cooperative transport problem into a collection of subproblems. Each subproblem consists of an ASV and load pair where each ASV is attached to the load at the same point. Since the system states evolve on a smooth manifold, we use the tools from differential geometry to model the holonomic constraint arising from the cooperative transport problem and the non-holonomic constraints arising from the ASV dynamics. We then synthesize distributed feedback control strategies using the proposed mathematical modeling framework to enable the team transport the load on a desired trajectory. We experimentally validate the proposed strategy using a team of micro ASVs.

Research paper thumbnail of Using control to shape stochastic escape and switching dynamics

Chaos: An Interdisciplinary Journal of Nonlinear Science, 2019

We present a strategy to control the mean stochastic switching times of general dynamical systems... more We present a strategy to control the mean stochastic switching times of general dynamical systems with multiple equilibrium states subject to Gaussian white noise. The control can either enhance or abate the probability of escape from the deterministic region of attraction of a stable equilibrium in the presence of external noise. We synthesize a feedback control strategy that actively changes the system's mean stochastic switching behavior based on the system's distance to the boundary of the attracting region. With the proposed controller, we are able to achieve a desired mean switching time, even when the strength of noise in the system is not known. The control method is analytically validated using a one-dimensional system, and its e ectiveness is numerically demonstrated for a set of dynamical systems of practical importance.

Research paper thumbnail of Optimal Path Planning in Time-Varying Flows Using Adaptive Discretization

IEEE Robotics and Automation Letters, 2018

Research paper thumbnail of Going with the flow: a graph based approach to optimal path planning in general flows

Research paper thumbnail of Tracking attracting manifolds in flows

Research paper thumbnail of Time and Energy Optimal Path Planning in General Flows

Robotics: Science and Systems XII

Research paper thumbnail of Tracking Attracting Lagrangian Coherent Structures in Flows

Robotics: Science and Systems XI, 2015

Research paper thumbnail of Zig-zag wanderer: Towards adaptive tracking of time-varying coherent structures in the ocean

2015 IEEE International Conference on Robotics and Automation (ICRA), 2015

Research paper thumbnail of Exploiting Stochasticity for the Control of Transitions in Gyre Flows

Robotics: Science and Systems XIV, 2018

We present a control strategy to control the intergyre switching time of an agent operating in a ... more We present a control strategy to control the intergyre switching time of an agent operating in a gyre flow. The proposed control strategy exploits the stochasticity of the underlying environment to affect inter-gyre transitions. We show how control can be used to enhance or abate the mean escape time and present a strategy to achieve a desired mean escape time. We show that the proposed control strategy can achieve any desired escape time in an interval governed by the maximum available control. We demonstrate the effectiveness of the strategy in simulations.